Real time energy consumption management of appliances, devices, and equipment used in high-touch and on-demand services and operations

ABSTRACT

An embodiment models and predicts energy consumption and provides recurring and realistic opportunities to reduce energy consumption throughout the work day or process cycle using user interfaces to convey positive and negative feedback in a controlled manner; and user experiences that reward positive changes with increased positive feedback and reduced negative feedback. Energy consumption of categories of appliances, devices, and equipment is considered a random variable. Using archived energy data, business data, and other related data, statistical modeling is used to create inverse cumulative probability distribution functions. An energy budget (consumption prediction) is computed so that it meets a probability p of the budget being exceeded during a given interval. When the budget is exceeded the feedback is negative, otherwise feedback is positive. Each budget is computed as the value b of the random variable such that the probability that the random variable will be less than or equal to b is 1−p.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates generally to systems and methods for creating andimplementing near real-time user interfaces (dashboards, alerts,reports, and visual and audio cues) for the management of appliances,devices, and other equipment used in high-touch and on-demand servicesand operations in order to reduce energy consumption whilesimultaneously meeting business goals. More specifically, the inventionrelates to how energy use patterns of appliances, devices and otherequipment are collected, analyzed and modeled, and how to provide toservice and operative workers real time feedback in a systematic andcontrolled manner to positively affect the energy use of appliances,devices, and other equipment used in their line of business whilesimultaneously meeting business goals.

2. Description of the Related Art

An energy management system (EMS) is used to instrument (collect data),monitor, and report on power consuming devices, appliances, andequipment as well as events and status conditions. Examples of powerconsuming devices, appliances, and equipment include refrigerationunits, ovens, toasters, cash registers, sewing machines, aircompressors, conveyors, kilns, dryers, extruders, LCD displays, lightingpanels, HVAC units, sensors, meters, controllers, and switches. Examplesof events and status conditions include door open, door closed, trashcompactor full, and trash compactor away. EMS data may be supplementedwith quantitative data including environmental and climate data such astemperature, cloud cover, sun rise and set, and relative humidity;non-energy utility use such as water, sewage, and telecommunications;performance data such as uptime, runtime or throughput; and businessdata such as purchases, orders, packaging, and routing. In some cases,an EMS is also used to control devices and appliances. For example, anHVAC may be controlled using real-time temperature and humidity readingsto achieve desired comfort levels, and parking lights may be controlledby business hours and local times of sun rise and sun set.

The EMS data are relayed to a data store or data center that can belocal to the collection device, building, business, or remotely hostedor distributed in the cloud. The data are typically accessed by a dataprocessing and reporting system and presented to a user who overseesfacilities. The user would access the presented data using a computerdevice such as a computer monitor, tablet, or smart phone using aninterface such as email, document viewer, web browser client, or otherhosted application that communicates to a backend server and data storewhere the backend server may be locally or remotely hosted and managed.

The facilities manager can look at total building energy use trends,drill down to specific devices or appliances, examine or identifycertain unusual conditions that may be manually or automaticallydetected, such as a malfunctioning HVAC unit, too low or too high roomtemperatures, or an oven left on when the building is unoccupied. Afacilities manager can then take actions to mitigate problems orprioritize retrofits and upgrades based on energy use patterns of thevarious devices, appliances, and equipment.

The facilities manager uses the EMS as both a strategic and tacticaltool. Interactions with the EMS may be sporadic or at regular intervalssuch as daily, weekly, monthly, or quarterly depending on the facilitiesmanager's responsibilities and priorities. However, employees working inhigh-touch and on-demand services and operations and whose work involvesthe regular use of multiple devices, appliances, and other equipment arenot able to use the EMS as part of their daily workflow for the benefitof the business or activity in which they are involved.

In service and manufacturing-based industries, there are often highlyvariable or erratic service and manufacturing requests that arrivethroughout the business day. EMS controls are usually not employed inthese lines of business, leaving operative and service workers, such asfood preparation and cooking machine operators, furnace, kiln, and dryeroperators, and first line managers in charge of managing the devices,appliances, and equipment of their trade on an ad hoc basis. Withoutfeedback as to the amount of energy used as a function of the businessactivity, these workers have little guidance or incentive to makechanges to their workflow behavior that would reduce energy use.

To better manage the energy use of appliances, devices, and otherequipment, operative workers require real-time information that isrelayed with minimal detail, that is easy to see and consume, and thatdoes not distract them from their task at hand. What information isneeded and how it is conveyed will depend on the work environment.Information may need to be relayed in visual and/or audio forms. Thelevel of details and types of information may need to be a simple cue,prompt, or instruction for “in-the-moment” feedback; simple summaries ofenergy use successes or issues by shift or other period of time may needto be available to line managers and operative workers to evaluateduring and after a shift or other period of time; scoreboards showinginformation for multiple teams at one or at various business locationsmay be needed to drive competitive behaviors; and richly detailedreports of energy use trends and patterns at various levels of theorganization to enable better understanding of the business and enableprocess improvements for reducing their carbon footprint.

To make beneficial changes in their behavior, service employees need areasonable and manageable amount of in-the-moment feedback that doesn'toverwhelm them or discourage them from making or continuing to makechanges in their work flow that will lead to energy savings whilesimultaneously meeting business goals. It is important to incentpositive changes made by a worker with positive feedback and a reductionin negative feedback. Therefore, there need to be methods and tools todefine and control the level or rate of feedback given to operativeemployees to prevent the “user fatigue” or “backlash” that could resultfrom too high a rate of in-the-moment feedback as well as methods todemonstrate and reward workflow changes that lead to energy savings.

The levels, or rates, of feedback may need to vary by any number offactors including appliance, device, equipment, industry, location,business unit, team, day of week, time of day, season, weather, orders,customer, and more. Since energy use fluctuates over time, there isinherent variability and unpredictability in the tasks an operator maydo at any given time. As the goal is to drive improvements in energyuse, robust statistical modeling and machine learning techniques thatcan learn and adapt over time to changing circumstances will be needed.

Furthermore, shift and line managers and executives will need to seereports of the performance by shift, day, or other periods of time thatinclude longitudinal analysis to assess energy savings improvements overtime.

SUMMARY

Various embodiments of the invention solve the above-mentioned problemsby providing an energy management system that submeters in nearreal-time the appliances, devices, and equipment used by service andoperative workers. The appliances, devices, and equipment are organizedinto categories of which all categories or a subset of categories may beused. To ensure that the near real-time feedback and recent summaries ofenergy use are relevant to the work at hand and that there are recurringand realistic opportunities for workers to reduce energy consumptionthroughout the work day or process cycle, discrete and independent timeintervals (such as 1 hour) are used in which the amount of feedback ofunder and over use may be globally or independently set and managed foreach category and interval of time. An energy budget for each categoryand for each time interval is provided where the budget is defined sothat it meets a specified probability p of the budget being exceeded(alternatively, the probability of the budget being met is 1−p) duringthat time interval. When the budget is exceeded the feedback is said tobe negative, and when the budget is met the feedback is said to bepositive.

One method of providing energy budgets considers energy use for eachcategory and interval pair a random variable. Using archived energydemand and consumption data, business data, and other related data,statistical modeling is used to create inverse cumulative probabilitydistribution functions for each random variable. Each budget can becomputed as the value b of the random variable such that the probabilitythat the random variable will be less than or equal to b is 1−p. Theunderlying statistical models may be updated continuously as the volumeof archived data expands over time. Other methods for providing energybudgets may use machine learning or other statistical techniques topredict or compute the budget that would be or should be used.

Once the budgets are available, various user interfaces (seen, heard, orotherwise perceived) can be used to convey: near real-time feedbackabout the under and over use of energy by category and interval so thatservice and operative workers may make in-the-moment changes in theirworkflow to reduce energy consumption; summary data regarding energyconsumption of categories for recent time intervals so that operativeworkers and managers may understand short term performance or impactwithin a shift or process cycle; a user experience that rewards andincents sustained energy savings behaviors; and richly detailed,historical reports by category or at the appliance, device, andequipment levels over various windows of time to help the businessbetter manage delivery and operations.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentlydisclosed systems and methods will become better understood withreference to the following description and appended claims, andaccompanying drawings where:

FIG. 1 is a block diagram illustrating an EMS that is using data frommultiple facilities as well as supplemental data for the monitoring andcontrol of the facilities and for providing near real-time feedback.

FIG. 2 is a block diagram illustrating a portion of an EMS submeteringand control and real-time feedback solution within a single facility orsite.

FIG. 3 is a block diagram illustrating a simplified configuration ofcontroller and submetering hardware in a facility.

FIG. 4 is a block diagram illustrating a server system providing remoteaccess for data and control and near real-time data to an EMS provider,facilities manager, service or operative worker, and business managersand business leaders.

FIG. 5 is a block diagram illustrating a system for providing nearreal-time data for energy consumption management of appliances, devices,and equipment used in high-touch and on-demand services and operations.

FIG. 6 contains screen shots illustrating various near real-time userinterface visualizations of energy consumption under and over use.

FIG. 7 and FIG. 8 contain screen shots illustrating various userinterface visualizations of summaries of energy consumption for recenttime intervals.

FIG. 9 contains tables that would be used to assist the operator inconfiguring visualizations of summaries of energy consumption for recenttime intervals.

FIG. 10 contains tables illustrating a method for a user experience anda concrete example of the method for rewarding and incenting sustainedenergy savings behaviors.

FIG. 11 contains an illustration of a user interface that incorporatesnear real-time visualizations of over and under use, visualizations ofsummaries of energy consumption for recent time intervals, and rewardsimagery.

Some figures illustrate diagrams of the functional blocks of variousembodiments. The functional blocks are not necessarily indicative of thedivision between hardware circuitry. Thus, for example, one or more ofthe functional blocks (e.g., processors or memories) may be implementedin a single piece of hardware (e.g., a general purpose signal processoror a block or random access memory, hard disk or the like). Similarly,the programs may be standalone programs, may be incorporated assubroutines in an operating system, may be functions in an installedsoftware package, and may reside in collocated or remotely locatedservers. It should be understood that the various embodiments are notlimited to the arrangements and instrumentalities shown in the drawings.

DETAILED DESCRIPTION OF THE INVENTION

The present invention may be understood more readily by reference to thefollowing detailed description of preferred embodiments of the inventionas well as to the examples included therein. Embodiments of theinvention provide systems and methods for the modeling of energy usepatterns and for the creation and conveyance of near real-time feedbackin a systematic and controlled manner for in-the-moment energyconsumption management of appliances, devices, and equipment used inhigh-touch and on-demand services and operations.

FIG. 1 is an illustration of an energy management system (EMS) 100 formonitoring and controlling one or more facilities 101 which may belocated in different geographic areas, and may receive energy in one ormore forms, for example electricity and natural gas, and from one ormore utilities. Utilities 102 use meters, typically at the utility sideof the interconnection point, to monitor energy consumption and demand,while the EMS 100 uses different meters to monitor energy consumptionand demand, typically at the facility side of the interconnection point.The EMS solution may also include sub-metering within a facility; thecollection of other, non-energy specific, data within a facility; andsupplemental data from third party data providers 103. Data streams aretransmitted to a data store 104 and are processed into consumable formsof data by backend, possibly distributed, servers 105 as designed anddirected by the EMS provider 106. An EMS user and operator, such as afacilities manager 107 accesses the prepared data using an interfacesuch as email, document viewer, web browser client, or other hosted ornative application. The operator can take responsive or correctiveaction based on the remotely received data provided by the remoteservers 104. A near real-time user interface 108 provides feedback toservice or operative workers 109 so that they can take immediateresponsive or corrective action to better manage energy use of theappliances, devices and equipment used in their work flow. The EMSprovider 106 also has access to the EMS 100 for the purpose of providingsupport, maintenance, and additional services.

In an embodiment, each facility has submetering and possibly control anduser interface hardware installed in it that is part of the energymanagement system and separate from utility-installed meters. FIG. 2 isan illustration of the portion of an EMS that may be found within asingle facility 200. A facility may be equipped with one or more meters201 and one or more controllers 202. A facility may be equipped with aninterface 203 for monitoring and control of the facility and with nearreal-time user interfaces 204 for appliances, devices and equipment usedby businesses within the facility. Typical measured data include, butare not limited to: total electric 205, gas 206, and water 207 utilityuse; natural gas or solar 208 and 209 power generation; facilitiesoperations such as irrigation 210, submetered utility use such as HVAC,refrigeration, ovens and other appliances, devices and equipment215-220; and status, events, and environmental data, 211-214. Thesubmetering equipment may include or interface with other devices andsensors to collect status, events, and environment data such as indoorand outdoor climate data, CO₂, and door open/closed. Total utility usemetering 205-207 may be reconciled against metering done by the utilityon its side of the interconnection point 221. Supplemental data (notshown in FIG. 2) may also be collected and sent to the energy managementsystem to be stored and processed in support of specific EMSapplications such as outdoor equipment control, weather normalizedenergy modeling, bill and rate verification, or order normalized energymodeling.

FIG. 3 is detailed schematic block diagram illustrating typical energymanagement system control and submetering hardware installed at afacility 300. A site controller 301 with embedded control algorithmscontrols electrical loads on multiple circuits 314 via light controlpanels (LCPs) 315. The site controller 301 is typically wired to commonvoltages at an electrical distribution panel (not shown) of a buildingfacility via a main line meter (power submeter) 306. The site controller301 includes memory 304 and CPU 305 for respectively storing andimplementing energy management algorithms. The algorithms acceptreal-time power and environmental variable measurements (such asreadings from thermostats 317) as inputs and determine how to controlthe power delivered on various circuits 314 and to control set pointsand other configurable settings such as enabling/disabling compressorstages on the thermostats 317. The site controller 301 may include apower supply (not shown) and one or more wired or wireless localcommunication and control interfaces 303 for controlling the circuits314 and thermostats 317. The thermostats 317 provide temperature andhumidity inputs to the site controller 301, and output control signalsto the HVAC units 316. A communication interface 302 providesbi-directional communication with a communication gateway 319, which inturn manages wired or wireless communications from the EMS server.

In an embodiment, one or more submeters 306 are coupled to the sitecontroller 301 either via wired or wireless connection. The submeter 306includes hardware and firmware to provide sampling functionality,including multiple analog-to-digital converters for multi-channel fastwaveform sampling of inputs such as current and voltage to produce asuite of measurements including demand, consumption, reactive power,power factor, and voltage. The submeter 306 includes wired or wirelesscommunication interfaces 307, current and voltage monitoring interfaces308, memory 309, CPU 310, and may also include a power supply (notshown). The current and voltage monitoring interfaces connect betweenthe power circuits being monitored and the A/D converter. Each channelmay be connected to a separate power circuit to monitor the flow ofcurrent through the circuit. The connection is typically made with acurrent transformer 313 at both a supply (i.e., hot) line and a return(i.e., neutral) line of the power circuit, which provides a waveformsignal that is representative of the current flow at the connectionpoint. The submeter 306 can receive voltage and current measurementsfrom the main line 311 as well as measurements from any of a number ofdevices 312 or groups of devices, as illustrated in FIG. 2 and describedherein. The controller 301 can also receive data directly from othersensors 318. Sampled data flows from the submetering devices 306 throughthe controllers 301 and on to the remote EMS servers via a wired orwireless network.

The submetering and control equipment can collect near real-timemeasurements (for example, every 1, 5, or 15 minutes, and preferably 1minute for this invention if used in a rapidly changing serviceenvironment such as a quick service restaurant). Each measurement has atime stamp, unit of measurement, and unique source identifier associatedwith it. Data from the same unique source comprise a time series whichis univariate if only one unit of measure is recorded or multivariate ifmultiple units of measure are sampled. Preferably, sampling intervalsare constant so that the time variable is implicit. Event data such asdoor open and door close may be irregularly spaced so that the timevariable must be explicit. The data collected by the submeteringequipment is sent to the energy management system via a wired orwireless network to be stored and processed.

FIG. 4 is an illustration of a portion of an EMS system external to afacility 400 with emphasis on server 401 and storage 405 systems used toprovide remote data access and control to users including EMS providers,facility managers, operators, service or operative workers, managers,and business leaders.

The servers 401 include processors 402, memory 403, and one or more I/Ointerfaces 404 for receiving data, transmitting control and supportingend-user applications over a network 406 where end-user applicationsinclude facility management or other end-user application access andconfiguration 409, EMS service administration 410, and near real-timedata for in-the-moment management of appliances, devices, and equipmentused in a particular line of business 411.

Storage abstractions 405 include one or more databases, including astreaming data database 405 a to store fresh data from the facilities407 and supplemental providers 408, a historic database 405 b to storearchived data from the facilities 407 and supplemental providers 408, aconfiguration and operating parameters database 405 c to store datarequired to run the EMS and end-user applications, a facilities database405 d to store data specific to each facility such as installationconfiguration and assets and points of contact, a reports database 405 eto store static prepared data used in end-user applications, astatistical models database 405 f to store predictive models used inend-user applications such as weather-normalized energy use predictionand near real-time data for in-the-moment management of appliances,devices, and other equipment used in high-touch and on-demand servicesand operations, a database for other analytics 405 g such as alarms, andstorage of other data 405 h.

The memory 404 stores software (tangible data and programs) forcreating, editing, and executing data and instructions necessary tooperate the EMS and run end-user applications including creation of datastructures, statistical models, reports and other data required toprovide near real-time data for in-the-moment management of appliances,devices, and other equipment used in high-touch and on-demand servicesand operations.

A business manager or operator 409 can access the user application andconfiguration software and detailed reporting software remotely ordirectly if the software is installed at the user-operated controlcenter. In an embodiment, the system is configured such that theoperator is able to configure near-real time user interfaces for asingle or multiple facilities and business applications.

The measured time series data are typically conceptualized and organizedfunctionally. Business logic is applied to the time series to createlogical, hierarchical, nested or other forms of structured data thatsupport EMS applications including monitoring, reporting, near real-timecontrol, and facilities maintenance. User interfaces to the EMS may beimplemented and conceptualized at a variety of levels including asset,appliance, device, and other equipment, or as groups or cross-groups ofassets, appliances, devices, and other equipment. The software must beflexible to support the wide variety of configurations that arise in theinstallation of submetering and control hardware at a facility. Forexample, if a facility has a single circuit dedicated to ovens,submetering be done on the circuit only to save costs; however, if thereare additional ovens on different circuits, those ovens may have beenseparately submetered. The software must accommodate these variationswhen organizing the data.

To streamline and minimize the amount of feedback given to operationalworkers, the time series data for the appliances, devices, and equipmentoperated by them are organized into categories of which all categoriesor a subset of categories may be used. If a subset of categories is tobe used, the business manager or operator may, in an embodiment,manually select in the software the categories to be used or selectconfigurations in the software so that the software will automaticallyselect or recommend which categories should be used, where the automatedidentification of categories is done systematically using a method thatincludes historic energy use and energy savings potential of eachcategory. In an embodiment, the operator can provide an ordering of theselected categories for use in display or reporting and apply uniquedisplay names and visual icons.

To ensure that the near real-time feedback and recent summaries ofenergy use are relevant to the work at hand and that there are recurringand realistic opportunities for operative workers to reduce energyconsumption throughout the work day or process cycle, feedback is givenin the context of discrete and non-overlapping time intervals. Thebusiness manager or operator may, in an embodiment, manually select inthe software the time interval or select configurations in the softwareso that the software will automatically select or recommend the timeinterval, where the automated identification of time interval is donesystematically using a method that includes business data such asindustry type and product specific to that facility and operator teamthat has been surveyed from the business as well as archived energy useand supplemental and business data.

Within each time interval, the amount of feedback of under and over use(also referred to as positive and negative feedback, respectively) maybe globally or independently set and managed for each category. Thebusiness manager or operator may, in an embodiment, manually select inthe software the feedback levels to a global value or to specific valuesor select configurations in the software so that the softwareautomatically selects the feedback levels. The automated selection isperformed systematically based on business data such as industry type,product, work hours specific to that facility and operative team,archived energy use and/or supplemental and other business data.

In an embodiment, to provide the desired, configured feedback regardingunder and over use of energy for a category within a given timeinterval, a budget is created where the budget for a category is definedso that it meets a specified probability p of the budget being exceededin that time interval where p was specified by the operator as theamount of feedback indicative of over use. Alternatively, the budget maybe defined so that it meets a specified probability 1−p of the budgetbeing met in that time interval. In an embodiment, a budget is the mean,or average, amount of energy consumed by a category for a given periodof time when p is set to be 0.5 (50% positive and 50% negativefeedback). To avoid user fatigue due to excessive negative feedback, theoperator may choose to use a smaller value of p such as 0.2 (20%).

FIG. 5 is an illustration showing a block diagram of a system forcreating near real-time data and feedback 500 including creation ofstatistical models of energy consumption and demand, energy budgets, andvisualizations and reports and other user interfaces for conveying nearreal time feedback. Available explanatory variables 501 and 507 andresponse variables 502 and 508 are identified and stored in theConfiguration and Operating Parameters database 505. The Modeler 503creates statistical models using archived explanatory variable data 501and archived response variable data 502 and configuration and operatingparameters 505 including category and time interval. The output of theModeler 503 are stored in the Statistical Models database 504, where theoutput includes the model, the selected explanatory variables,parameters if the model is parametric, and other descriptors if themodel is non-parametric. The Predictor 506 creates real time predictionsof the response variables (energy demand and consumption) which includecomputations of budgets which are predictions of energy consumption,using real-time explanatory variable data 507, models from theStatistical Model database 504, and configuration and operatingparameters including category, time interval, and feedback parameter p.The predictions and budgets are used, with archived data and real timedata, to create near real time data 509 such as visualizations andreports and other user interfaces that are provided to the end user 510who may be a service or operative employee, managers, or businessleader.

In an embodiment of the Modeler 503 and Predictor 506, energyconsumption for each category and interval pair is considered a randomvariable and the budget is derived from the probability distributionfunction used to model the random variable. Using archived energy demandand consumption data, business data, and other related data, statisticalmodeling is used to create inverse cumulative probability distributionfunctions (also known as quantile functions) for each random variable.Each budget is computed as the value b of the random variable such thatthe probability that the random variable will be less than or equal to bis 1−p. The underlying statistical models are updated, preferablycontinuously as the volume of archived data expands over time, to adaptto changes in the business and appliances, devices, and other equipment.In an embodiment, the choice of the underlying model depends on the dataitself; when there is little archived data and hence few data samples,it is preferable to use non-parametric, empirical quantile functionssuch as those elaborated in Hyndman and Fan, “Sample Quantiles inStatistical Packages” American Statistician, 1996 and to recompute orupdate the quantile functions with each newly acquired data sample.Other embodiments for deriving energy budgets may use machine learningor other statistical techniques to predict or compute the budget thatwould be or should be used to achieve the desired levels of negative andpositive feedback.

Once the predictions (budgets) are available, various user interfaces(seen, heard, or otherwise perceived) can be used to convey: nearreal-time feedback about the under and over use of energy by categoryand interval so that service and operative workers may makein-the-moment changes in their workflow to reduce energy consumption;summary data regarding energy consumption of categories for recent timeintervals so that operative workers and managers may understand shortterm performance or impact within a shift or process cycle; a userexperience that rewards and incents sustained energy savings behaviors;and richly detailed, historical reports by category or at the appliance,device, and equipment levels over various windows of time to help thebusiness better manage delivery and operations.

In an embodiment, the operational worker accesses near real-time datainterfaces in their work environment. What information is needed and howit is conveyed will depend on the work environment. It is crucial thatdata conveyed to operative workers are not over detailed and that themode or manner of conveyance does not distract a worker from the task athand. Various means can be provided to convey in near real-time energyover use for a given category. For example: if workers have line ofsight to dashboard or kiosk types of displays, an embodiment is a gaugeform of data visualization; if workers use visual displays forms ofinformation in an out of the way area, an embodiment is a simple visualcue, such as a light, that turns on if energy is being over used; ifworkers rely on audio signals, an embodiment conveys an over-use messagein an audible manner to prompt the workers to make changes such asturning off an appliance.

One function of near-real time visualizations of energy over and underconsumption is to convey quickly and with minimal detail which categoryis consuming or is at risk of consuming too much energy. FIG. 6 showstwo different embodiments of visualizations of near-real time energyconsumption against budget 600, both of which use a gauge for eachcategory where the real-time data are normalized to each budget toeliminate variations of magnitude across categories and where coloroverlays reinforce the current state of over consumption (e.g. yellow orred) and under consumption (e.g. green) as compared to predictedconsumption.

The first visualization 601 uses a gas-tank like (depletion paradigm)gauge for each category with a given “fuel budget” for each timeinterval. At the beginning of each time interval, for example thebeginning of each hour for hourly intervals, the gauge is reset to full(F) at the far right. Each gauge is continuously updated to showconsumption as a function of budget depletion. In 601, the dial willmove from full towards empty (right to left) over the time interval asenergy is consumed. The gauges have color overlays intended as promptsto users to reduce energy use. In 601, assume that the time interval isone hour and the current time is half-way through the current hour: thegauge is green in 601 a because energy consumption is at or belowconsumption expected against the given budget; the gauge is yellow in601 b because energy consumption is higher than expected against thegiven budget and steps should be taken now to get energy use back ontrack; and the gauge is red in 601 c as energy consumption has exceededthe entire budget allocated for that hour.

The second visualization 602 uses an accumulation paradigm for thegauges for each category with a given budget “limit” for each timeinterval. At the beginning of each time interval, for example thebeginning of each hour, the gauge is reset to 0% at the far left. Eachgauge is continuously updated to show consumption as accumulationtowards the limit. In 602, the dial will move from 0% towards 100% (leftto right) over the time interval as energy is consumed. The gauges havecolor overlays intended as prompts to users to reduce energy use. In602, assume that the time interval is one hour and the current time ishalf-way through the current hour: the gauge is green in 602 a becauseenergy consumption is at or below consumption expected against the givenbudget; the gauge is yellow in 602 b because energy consumption ishigher than expected against the given budget and steps should be takennow to get energy use back on track; and the gauge is red in 602 c asenergy consumption has exceeded the entire budget allocated for thathour.

The a priori probability that a budget is exceeded for a category in agiven time period is p, where p is the very same control parameter setby the operator to create the budgets. The amount of yellow and redshown to the user is thus controlled by p. If users respond to thefeedback and make changes to their work flow that reduce energy use,then in practice, then the users will be rewarded with more positivefeedback (green) and less negative feedback (yellow and red).

One function of summary data visualizations of energy consumption forrecent time intervals is to help operative workers and managersunderstand short term performance or impact within a shift or processcycle. The visualizations are intended to convey quickly and withminimal detail the aggregate over or under consumption of energy of aset of categories over recent intervals. In an embodiment, an operatorcan configure the visualization to show I prior intervals over a fixedtime period. For example, if the interval size is one hour, the operatormay select a summary of all intervals during the past 24 hours or asummary of only the completed intervals within the current calendar day(I=24). In an embodiment the summary for the end state of one intervalis conveyed with a simple color overlay and/or by magnitude or othercue. In an embodiment, there are N end states for the end of each timeinterval, where an end state is the number of budgets not exceeded atthe end of the interval. The operator can configure each interval endstate to have a specific color overlay or other cue. In an embodiment,the operator can configure the visualization to exclude the data fromspecific categories, such as Total Building or Main Load, as operativeworkers are unable to directly control or be responsible for the energyconsumption of some categories but for which a near real-time gaugevisualization provides data of interest. If the number of budgets beingtracked in this visualization is B, then the number of end states isN=B+1. In an embodiment, the operator can configure the visualizationwith overlays that delineate shifts or process cycles.

FIG. 7 and FIG. 8 show two different embodiments of visualizations, bothof which use a colored tile for each past interval to represent thestate of energy consumption against budget at the end of that interval.In both visualizations, the time interval is one hour, 24 hours of pastdata are displayed, and color only is used to convey state.Representations of state that vary in color and magnitude or inmagnitude only are not shown. When data are not completely known (e.g.current interval or future intervals) or state is otherwise unknown, abackground color is used for that tile.

In FIG. 7, linear visualizations are used to provide summaries of energyconsumption for recent intervals. In 701 four colors are used to conveythe ending state of one interval where the end state is the number ofbudgets that were not exceeded and there were three categories. If zerobudgets were exceeded, then green is shown; if any one budget wasexceeded then yellow is shown; if any two budgets were exceeded thenorange is shown; if all budgets were exceeded then red is shown. In 702,three colors are used to convey the ending state of one interval wherethe end state is the number of budgets that were not exceeded and therewere three categories. If zero budgets were exceeded, then green isshown; if any one budget was exceeded then yellow is shown; if any twobudgets were exceeded then yellow is shown; if all budgets were exceededthen red is shown. In 703 an overlay of shifts is applied to 702 so thatoperative workers and line managers may better understand short termperformance and impact within a shift or process cycle.

The embodiment shown in FIG. 8 conveys the data as a circular, or piechart visualization where 801 represents the same data as 701, 802represents the same data as 702, and in 803 and overlay of shifts isapplied to 801 so that that operative workers and line managers maybetter understand short term performance and impact within a shift orprocess cycle. When data samples are not complete (e.g. current timeinterval or future intervals) or state is otherwise unknown, abackground color is used for that slice.

The a priori probability that a budget is exceeded for a category in agiven time period is p, where p is the very same control parameter setby the operator to create the budgets. The choice of the number ofcategories and number of colors or cues to use in the summaries ofenergy consumption for recent intervals has a direct impact on thevariability of the feedback provided to the user. The probabilities ofthe states shown to the user are thus controlled by p and the number ofcategories and number of states. It is straightforward to compute theprobability of a state given the number of categories, the number ofstates, and the probability p. An embodiment provides the operatorinformation regarding what to expect in the visualization so that theoperator can make informed decisions when configuring of thevisualization for short term energy consumption use.

FIG. 9 shows tables of data that would be used to assist the operator inconfiguring color overlay visualizations of summaries of energyconsumption for recent time intervals. In example 901, there are threecategories and four end states (S1=green if all budgets are met for thethree categories; S2=yellow if two budgets only are met for the threecategories, S3=orange if one budget only is met for the threecategories; and S4=red if zero budgets are met for the threecategories). In an embodiment, the data provided to the user wouldinclude the probabilities of each of the four end states where the endstate of each category is modeled as a Bernoulli random variable that isparameterized by the probability p of the budget being exceeded, and thethree categories are assumed to be statistically independent. In thisexample the random variables are assumed to be statistically independentand identically distributed (iid). In example 902, there are threecategories and three end states (S1=green if all budgets are met for thethree categories; S2=yellow if one or two budgets are met for the threecategories; and S3=red if zero budgets are met for the threecategories). As in example 901, the table shows the probabilities ofeach of the three end states where the end state of each category aremodeled as iid Bernoulli random variables that are parameterized by theprobability p of the budget being exceeded.

If users respond to the feedback and make changes to their work flowthat reduce energy use, then in practice, the users will be rewarded inthe summary visualization with more positive feedback (more states whereall budgets or all but one budgets were met) and less negative feedback(states where two or more budgets were not met).

To provide a user experience that rewards and incents sustained energysavings behaviors, an embodiment uses changing imagery on a dashboard toconvey the cumulative impact of recent time intervals. FIG. 10illustrates a method for selecting reward imagery based on recent pastperformance where recent past performance is based on the set, orsubset, of the number of time intervals displayed in the summary ofenergy consumption for recent time intervals.

In an embodiment, the operator can configure the number K of most recenttime intervals to be used in selecting and displaying imagery on adashboard where the maximum number for K should be the maximum number Iconfigured in the summary visualization. The state of each intervaldisplayed in the summary of energy consumption is conveyed using a coloroverlay (or other cue). In an embodiment, a numerical value isassociated with each state, 1000, in addition to the color associatedwith that state as used for the overlay in the summary. The numericalvalues represent the “goodness” of the end state. For example, in 1002,there are four end states S1-S4 for 3 categories where S1 is the statefor no budgets exceeded, S2 is the state for one budget only wasexceeded, S3 is the state for two budgets exceeded, and S4 is the statefor all budgets exceeded. The states S1-S4 are sorted by “goodness”where the best state is S1 and the worst state is S4 and numericalvalues are assigned to the states in strictly decreasing order (orstrictly increasing order) so that the best state has the highest value(or lowest value) and the worst state has the lowest value (or highestvalue).

In an embodiment, the operator configures the amount of imagery (numberof levels M) to be displayed and the imagery itself. The range of levelsthat can be displayed can be determined systematically and suggested tothe operator based on the settings of N and K. For the past K intervals,the values corresponding to each interval state are summed andnormalized to a range of 0 to 1 to create ratio R. Scalar quantizationmethodology is used to systematically determine the bins associated witheach level and the imagery to be displayed. As shown in 1001, the M binsare defined the range of the value R. Once a bin has been selected, theAward Level is designates the imagery to be displayed. The award leveland imagery should be directly correlated to the overall goodness of therecent history. That is, if the interval state values are assigned sothat the highest values are correlated to the highest “goodness” of astate, then the ratios with the highest values should correspond to themost rewarding imagery; if the interval state values are assigned sothat the highest values are correlated to the least goodness of a state,then the ratios with the highest values should be correspond to theleast rewarding imagery.

To walk through a complete example: in 1002, there are 3 budgets beingtracked (B=3) and therefore four end states (N=4) where S1 is the statefor no budgets exceeded, S2 is the state for one budget only wasexceeded, S3 is the state for two budgets exceeded, and S4 is the statefor all budgets exceeded. The states S1-S4 are sorted by “goodness”where the best state is S1 and the worst state is S4 and numericalvalues are assigned to the states in strictly decreasing order 3, 2, 1,0 so that the best state has the highest value. In 1004, K=6 of the mostrecent time intervals are tracked. The values and colors for each of thesix end states are taken from 1002. In 1005, the sum of the values in1004 is 11 and the maximum possible value is 3*6=18. The normalizedvalue R=0.61 is computed as the sum 11 divided by the maximum value 18.In 1003, bins for M=8 levels are specified where the bins are uniformlyspaced (uniform scalar quantization is used to define the bins assumingR is uniformly distributed). The award levels would range from the leastrewarding imagery assigned to the bin with the lowest ratios R to themost rewarding imagery assigned to the bin with the highest ratios R.The bin for R=0.61 in 1003 is Bin 4 as 0.61 is less than 0.625 andgreater than or equal to 0.5. Therefore the award level is L4 and theimagery to be displayed should be 3 levels “degraded” from the bestimagery associated with award level L7 and four levels “improved” fromthe worst imagery associated with award level L0. In practice, thedistribution of R is not uniform, and an embodiment would use scalarquantization or adaptive scalar quantization methodologies to computethe bins to optimize the user experience so that all reward levels areconveyed and feedback given appropriately.

In FIG. 11, an embodiment with all of the visualization elementsdiscussed above is illustrated where real-time gauges (depletionparadigm) with color overlays are given prominent display space for inthe moment feedback, and summaries of recent time intervals and rewardimagery are given secondary and tertiary placement shift relatedfeedback.

To evaluate overall performance, shift and line managers and executiveshave access to reports by shift, day, and other periods as well as byorganizational structure, to assess daily workflows and energy savingsimprovements via an advanced reporting user interface.

The present invention is described above with reference to blockdiagrams and operational illustrations of methods and devices forcreating real time data for in-the-moment management of appliances,devices, and equipment used in a particular line of business. It isunderstood that each block of the block diagrams or operationalillustrations, and combinations of blocks in the block diagrams oroperational illustrations, may be implemented by means of analog ordigital hardware and computer program instructions. These computerprogram instructions may be stored on computer-readable media andprovided to a processor of a general purpose computer, special purposecomputer, ASIC, or other programmable data processing apparatus, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, implements thefunctions/acts specified in the block diagrams or operational block orblocks. In some alternate implementations, the functions/acts noted inthe blocks may occur out of the order noted in the operationalillustrations. For example, two blocks shown in succession may in factbe executed substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

At least some aspects disclosed can be embodied, at least in part, insoftware. That is, the techniques may be carried out in a specialpurpose or general purpose computer system or other data processingsystem in response to its processor, such as a microprocessor, executingsequences of instructions contained in a memory, such as ROM, volatileRAM, non-volatile memory, cache or a remote storage device.

Routines executed to implement the embodiments may be implemented aspart of an operating system, firmware, ROM, middleware, service deliveryplatform, SDK (Software Development Kit) component, web services, orother specific application, component, program, object, module orsequence of instructions referred to as “computer programs.” Invocationinterfaces to these routines can be exposed to a software developmentcommunity as an API (Application Programming Interface). The computerprograms typically comprise one or more instructions set at varioustimes in various memory and storage devices in a computer, and that,when read and executed by one or more processors in a computer, causethe computer to perform operations necessary to execute elementsinvolving the various aspects.

A machine-readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods. The executable software and data may be stored invarious places including, for example, ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data may be storedin any one of these storage devices. Further, the data and instructionscan be obtained from centralized servers or peer-to-peer networks.Different portions of the data and instructions can be obtained fromdifferent centralized servers and/or peer-to-peer networks at differenttimes and in different communication sessions or in a same communicationsession. The data and instructions can be obtained in entirety prior tothe execution of the applications. Alternatively, portions of the dataand instructions can be obtained dynamically, just in time, when neededfor execution. Thus, it is not required that the data and instructionsbe on a machine-readable medium in entirety at a particular instance oftime.

Examples of computer-readable media include but are not limited torecordable and non- recordable type media such as volatile andnon-volatile memory devices, read only memory (ROM), random accessmemory (RAM), flash memory devices, floppy and other removable disks,magnetic disk storage media, optical storage media (e.g., Compact DiskRead-Only Memory (CD ROMS), Digital Versatile Disks (DVDs), etc.), amongothers.

In general, a machine readable medium includes any mechanism thatprovides (e.g., stores) information in a form accessible by a machine(e.g., a computer, network device, personal digital assistant,manufacturing tool, any device with a set of one or more processors,etc.).

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement the techniques. Thus, thetechniques are neither limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system.

Although the present invention has been described in considerable detailwith reference to certain preferred versions thereof, other versions arepossible. Therefore, the spirit and scope of the appended claims shouldnot be limited to the description of the preferred versions containedherein. The reader's attention is directed to all papers and documentswhich are filed concurrently with this specification and which are opento public inspection with this specification, and the contents of allsuch papers and documents are incorporated herein by reference.

All the features disclosed in this specification (including anyaccompanying claims, abstract, and drawings) may be replaced byalternative features serving the same, equivalent or similar purpose,unless expressly stated otherwise. Thus, unless expressly statedotherwise, each feature disclosed is one example only of a genericseries of equivalent or similar features.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specificfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 U.S.C §112, sixth paragraph. In particular, the use of“step of” in the claims herein is not intended to invoke the provisionsof 35 U.S.C §112, sixth paragraph.

1-93. (canceled)
 94. A method of controlling energy consumption at afacility having energy consuming equipment and an on-site humanequipment operator capable of manually controlling the energyconsumption of the equipment, the method comprising: sub-metering theequipment at the facility to produce time series data representingenergy use during each time interval of a plurality of successivenon-overlapping time intervals; repeatedly calculating, during and foreach time interval, the total energy consumption of the equipment usingthe time series data; receiving a selected value indicative of aprobability P that the total energy consumption of the equipment willexceed an energy budget B during each time interval; calculating theenergy budget B from a statistical model of energy consumption for theequipment, wherein the energy budget B is a function of the probabilityP; repeatedly comparing, during and for each time interval, the totalenergy consumption of the equipment to the energy budget B to determinethe progress made towards reaching the energy budget B for each timeinterval; and providing real-time feedback to the human equipmentoperator at the facility that indicates the progress made towardsreaching the energy budget B within each time interval, wherein the realtime feedback provides the human equipment operator with informationthat enables the human equipment operator to make real-time decisionsabout how to control the equipment in order to minimize energyconsumption.
 95. The method of claim 94, further comprising: creatingmultiple statistical models, each of the multiple statistical modelscorresponding to one of multiple budgets B; and calculating the multiplebudgets B from their corresponding statistical models, wherein each ofthe multiple budgets B corresponds to one or more items of the energyconsuming equipment.
 96. The method of claim 95, wherein each of themultiple budgets B corresponds to an equipment category.
 97. The methodof claim 94, further comprising: creating the statistical model ofenergy consumption based on one or more archived explanatory variables,archived consumption data, or archived demand data, wherein thestatistical model of energy consumption includes an inverse cumulativeprobability distribution function, and wherein the energy budget B iscalculated by applying the probability P to the inverse cumulativeprobability distribution function.
 98. The method of claim 97, furthercomprising: periodically recalculating the energy budget B to reflectchanges of the one or more of explanatory variables, archivedconsumption data, or archived demand data.
 99. The method of claim 94,further comprising: periodically updating the statistical model ofenergy consumption in order to adjust the energy budget B.
 100. Themethod of claim 94, further comprising: updating the statistical modelof energy consumption to include one or more changes in businessoperation or equipment.
 101. The method of claim 94, further comprising:creating the statistical model of energy consumption based on anon-parametric empirical quantile function.
 102. The method of claim 94,further comprising: creating the statistical model of energy consumptionbased on a parametric quantile function.
 103. The method of claim 94,wherein the real-time feedback includes: displaying a graphicaldepletion gauge that indicates to the human equipment operator how muchof the energy budget B remains with respect to the calculated energybudget B within each time interval.
 104. The method of claim 103,wherein the real-time feedback includes: repeatedly displaying a colorcode that is indicative of the total energy consumption of the equipmentrelative to the energy budget B during each time interval.
 105. Themethod of claim 104, wherein the color code is superimposed over thegraphical depletion gauge.
 106. The method of claim 94, wherein thereal-time feedback includes: displaying a graphical accumulation gaugethat indicates to the human equipment operator how much of the energybudget B has been consumed with respect to the calculated energy budgetB.
 107. The method of claim 106, wherein the real-time feedbackincludes: repeatedly displaying a color code that is indicative of thetotal energy consumption of the equipment relative to the energy budgetB during each time interval.
 108. The method of claim 107, wherein thecolor code is superimposed over the graphical accumulation gauge. 109.The method of claim 94, wherein providing the real-time feedbackincludes: generating an audible sound that indicates to the humanequipment operator the total energy consumption of the equipmentrelative to the energy budget B during each time interval.
 110. Themethod of claim 94, wherein multiple groups of the equipment aresub-metered, each group having its own energy budget B, furthercomprising: determining, for each group of equipment, the number ofgroups that exceeded the corresponding energy budget B for each timeinterval; and graphically displaying, on a time scale delineating eachof multiple time intervals, an indicator of the number of equipmentgroups that exceeded the corresponding energy budget B during each timeinterval.
 111. The method of claim 110, wherein the time scale includesshift labels.
 112. The method of claim 94, wherein multiple groups ofthe equipment are sub-metered, each group having its own energy budgetB, further comprising: determining, for each group of equipment, thenumber of groups that met the corresponding energy budget B over a fixednumber of previous time intervals; and graphically displaying one of aplurality of images indicative of the number of groups of equipment thatmeet their corresponding budget B.
 113. A computer program product forcontrolling energy consumption at a facility having energy consumingequipment and an on-site human equipment operator capable of manuallycontrolling the energy consumption of the equipment, wherein theequipment at a facility is sub-metered to produce time series datarepresenting energy use during each time interval of a plurality ofsuccessive non-overlapping time intervals, comprising: a computer usablemedium having computer readable program code embodied in the computerusable medium for causing an application program to execute on acomputer system, the computer readable program code means comprising:computer readable program code for repeatedly calculating, during andfor each time interval, the total energy consumption of the equipmentusing the time series data; computer readable program code for receivinga selected value indicative of a probability P that the total energyconsumption of the equipment will exceed an energy budget B during eachtime interval; computer readable program code for calculating the energybudget B from a statistical model of energy consumption for theequipment, wherein the energy budget B is a function of the probabilityP; computer readable program code for repeatedly comparing, during andfor each time interval, the total energy consumption of the equipment tothe energy budget B to determine the progress made towards reaching theenergy budget B for each time interval; and computer readable programcode for providing real-time feedback to the human equipment operator atthe facility that indicates the progress made towards reaching theenergy budget B within each time interval, wherein the real timefeedback provides the human equipment operator with information thatenables the human equipment operator to make real-time decisions abouthow to control the equipment in order to minimize energy consumption.114. The computer program product of claim 113, further comprising:computer readable program code for creating multiple statistical models,each of the multiple statistical models corresponding to one of multiplebudgets B; and computer readable program code for calculating themultiple budgets B from their corresponding statistical models, whereineach of the multiple budgets B corresponds to one or more items of theenergy consuming equipment.
 115. The computer program product of claim114, wherein each of the multiple budgets B corresponds to an equipmentcategory.
 116. The computer program product of claim 113, furthercomprising: computer readable program code for creating the statisticalmodel of energy consumption based on one or more archived explanatoryvariables, archived consumption data, or archived demand data, whereinthe statistical model of energy consumption includes an inversecumulative probability distribution function, and wherein the energybudget B is calculated by applying the probability P to the inversecumulative probability distribution function.
 117. The computer programproduct of claim 96, further comprising: computer readable program codefor periodically recalculating the energy budget B to reflect changes ofthe one or more of explanatory variables, archived consumption data, orarchived demand data.
 118. The computer program product of claim 113,further comprising: computer readable program code for periodicallyupdating the statistical model of energy consumption in order to adjustthe energy budget B.
 119. The computer program product of claim 113,further comprising: computer readable program code for updating thestatistical model of energy consumption to include one or more changesin business operation or equipment.
 120. The computer program product ofclaim 113, further comprising: computer readable program code forcreating the statistical model of energy consumption based on anon-parametric empirical quantile function.
 121. The computer programproduct of claim 113, further comprising: computer readable program codefor creating the statistical model of energy consumption based on aparametric quantile function.
 122. The computer program product of claim113, wherein the real-time feedback includes: computer readable programcode for displaying a graphical depletion gauge that indicates to thehuman equipment operator how much of the energy budget B remains withrespect to the calculated energy budget B within each time interval.123. The computer program product of claim 122, wherein the real-timefeedback includes: computer readable program code for repeatedlydisplaying a color code that is indicative of the total energyconsumption of the equipment relative to the energy budget B during eachtime interval.
 124. The computer program product of claim 123, furthercomprising: computer readable program code for superimposing the colorcode over the graphical depletion gauge.
 125. The computer programproduct of claim 113, wherein the real-time feedback includes: computerreadable program code for displaying a graphical accumulation gauge thatindicates to the human equipment operator how much of the energy budgetB has been consumed with respect to the calculated energy budget B. 126.The computer program product of claim 125, wherein the real-timefeedback includes: computer readable program code for repeatedlydisplaying a color code that is indicative of the total energyconsumption of the equipment relative to the energy budget B during eachtime interval.
 127. The computer program product of claim 126, furthercomprising: computer readable program code for superimposing the colorcode over the graphical accumulation gauge.
 128. The computer programproduct of claim 113, wherein providing the real-time feedback includes:computer readable program code for generating an audible sound thatindicates to the human equipment operator the total energy consumptionof the equipment relative to the energy budget B during each timeinterval.
 129. The computer program product of claim 113, whereinmultiple groups of the equipment are sub-metered, each group having itsown energy budget B, further comprising: computer readable program codefor determining, for each group of equipment, the number of groups thatexceeded the corresponding energy budget B for each time interval; andcomputer readable program code for graphically displaying, on a timescale delineating each of multiple time intervals, an indicator of thenumber of equipment groups that exceeded the corresponding energy budgetB during each time interval.
 130. The computer program product of claim129, wherein the time scale includes shift labels.
 131. The computerprogram product of 94, further comprising: computer readable programcode for sub-metering multiple groups of the equipment, each grouphaving its own energy budget B, further comprising: computer readableprogram code for determining, for each group of equipment, the number ofgroups that met the corresponding energy budget B over a fixed number ofprevious time intervals; and computer readable program code forgraphically displaying one of a plurality of images indicative of thenumber of groups of equipment that meet their corresponding budget B.132. A computer program product for controlling energy consumption at afacility having multiple categories of energy consuming equipment and anon-site human equipment operator capable of manually controlling theenergy consumption of the equipment, the method comprising, wherein themultiple categories of equipment at the facility are sub-metered toproduce time series data representing energy use during each timeinterval of a plurality of successive non-overlapping time intervals,comprising: a computer usable medium having computer readable programcode embodied in the computer usable medium for causing an applicationprogram to execute on a computer system, the computer readable programcode means comprising: computer readable program code for repeatedlycalculating, during and for each time interval, the total energyconsumption of each category of the equipment using the time seriesdata; computer readable program code for receiving, for each of themultiple categories of equipment, a value indicative of a probability Pthat the total energy consumption of the equipment category will exceedan energy budget B for the equipment category, during each timeinterval; computer readable program code for calculating, for each ofthe multiple categories of equipment, the energy budget B, from astatistical model of energy consumption for the equipment category wherethe energy budget B for each equipment category is a function of theselected probability P for each equipment category; computer readableprogram code for comparing, during and for each time interval, the totalenergy consumption of each equipment category to the energy budget B foreach equipment category to determine if the energy budget B for eachcategory was exceeded for each time interval; and computer readableprogram code for providing real-time feedback to the human equipmentoperator at the facility that indicates the number of equipment categorybudgets B that were met within each time interval, wherein the real timefeedback provides the human equipment operator with information thatenables the human equipment operator to make real-time decisions abouthow to control the equipment in order to minimize energy consumption.